The recent progress in the area of digital imaging and computing technology has resulted in an exponential increase in digital media data. Texture is one of the important features to facilitate search and classification of image data. Various texture descriptors are available that facilitate in determining important visual cues of the images texture like surface properties and orientation, which may be further used in various applications such as scene interpretation, image segmentation and object-detection in the field of computer vision. Examples of available texture descriptors includes, Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), Rotation invariant-Local Binary Pattern (RLBP) and Rotation Invariant Histograms of Oriented Gradients.
However, the major challenge in the field of texture determination is that images are not always captured from the identical viewing angle in the real world. In many domains, such as textiles, nature photos and satellite images, the orientation of the objects can undergo significant rotation. And, it is highly onerous to ensure that captured images have the same degree of orientation between each other.
Conventionally, the existing texture descriptors are either sensitive to image rotation or are computationally expensive. HOG and LBP are two most commonly used texture descriptors in computer vision. Both the descriptors have low computational complexity, but are sensitive to rotation of texture patterns in images. There are a few rotation-invariant textures descriptors like RLBP and Rotation invariant HOG, which initially computes the LBP and HOG descriptors respectively, and introduce additional steps to align the local histograms to achieve rotation-independence. The additional step for achieving rotation invariance makes RLBP and Rotation Invariant HOG computationally expensive. Further, some of the texture features have high dimensionality resulting in higher computational and storage costs. For example, LBP and RLBP are represented as 256 dimensional 36 dimensional vectors respectively.